Repreceive the urban environment by social media posts
–A possible approach to quantify massive qualitative urban experience
Yuanzhao WANG
Supervisor: Carole Voulgaris
Abstract
Understanding and reflecting urban dwellers’ happiness, and perception of urban space and life in urban planning has long been a challenge in China. The Chinese government has progressively brought attention to public participation and views, which had previously been ignored. However, government approaches for fostering public engagement remain scarce, while enthusiasm among individuals to express feelings and comments on government planning is lacking. In this context, my research project proposes measuring individual citizens’ emotions and characteristics of the built environment,the proximity to various urban amenities, to explore the relationships between citizens’ sentiments, urban space, and urban activities. This approach can be applied to gather public feelings in the urban planning process through platforms, as a way to quantify the quality of urban life. By establishing a quantitative urban model, the happiness of urban residents(the sentiment of social media post) can become an indicator and standard as a way of bottom-up public participation, guiding planners’ decisions to design a healthy, sustainable and people-centered city.
Introduction
In China, the early stage of urbanization was subject to the national ambition and development, whose aim at boosting the economic production and consumption. As such, hundreds and thousands of cities and towns were built to serve for this purpose that plays an important role in improving the national economy based on the scarification of natural resource and incline of factory production. However, since the decreasing demand for industrial production and the arise of urban awareness of residents happiness, Chinese government increasingly focus on citizen wellbeing, urban governance and the urban environment.
The new urban policies in China shifted the emphasis of urbanization away from economic development and toward human-centric development, improving residents’ wellbeing and building new ecological smart cities. However, the evaluation of residents’ wellbeing and urban planning process remains top-down and entirely conducted by governments and experts. The qualitative surveys and reports could only cover a small proportion of population, and it becomes even harder and time-consuming as the population in cities and towns reached 848 millions.
With the introduction of social media data and machine learning technologies, new methods for studying urban spatial patterns and residents’ life have emerged. In 2017, there were more than 753 million people have access to mobile internet, while 68% of them frequently use social media platform. The AI-based technology such as sentiment analysis could be used to extract individual feelings from text-based information, as a form of public perception of urban space, contributing to bottom-up engagement and people-oriented urban planning.
Literature Review
Wellbeing as a Measure of Health and the Built environment
Scientists have historically measured well-being using objective indicators (e.g., GDP, health, employment, literacy, poverty) and increasing measured subjective well-being that influences individual life. Modern measures of well-being that account for cognitive evaluations (i.e., evaluative well-being) and reactions to experiences (i.e., experienced well-being) have therefore become the “currency of a life that matters” (Rath et al., 2010). As the concept of well-being develops, the indices including physical health, mental health, air quality and more are increasingly used, implying a strong relationship between health and residents’ well-being (Diener et al., 1999; Lawless & Lucas, 2010). Some studies found that population density may affect well-being on the city level (Florida et al., 2013). Mouratidis (2018) argues that compact urban form with better public transport, accessibility, the mix of land uses, and density positively influences neighborhood well-being. Social and human capital, considered significant drivers of urban well-being, can be affected by safety, educational opportunities, and access to arts and culture (Leyden et al., 2011; Florida et al., 2013). Other aspects of urban infrastructure (such as roads and transportation) impact commute time and connectedness, both of which are related to happiness (Yin et al., 2021; Gim, 2021).
Quantitative urban measure of the built environment
The measurement of the built environment is constructed by a variety body of indices to address different urban issues. Cervero and Kockelman’s developed initial “three Ds” (density, design, and diversity) in 1997 to evaluate the existing urban built environment. Edwing et al. expanded on this concept by adding two Ds (destination accessibility and transportation distance) (Ewing & Cervero, 2001; Ewing et al., 2009). More Ds were added afterwards to reflect the changing built environment, such as Demand management and Demographics (Ewing & Cervero, 2010). Scholars have modified the list of variables based on these quantitative frameworks to comprehensively examine the built environment while addressing various urban issues and topics. Some research used relative entropy to discern compactness from sprawl in the built environment (Tsai, 2005). Others used a multi-metric urban intensity index at a metropolitan scale, which included land use, infrastructure, and landscape variables in addition to density and compactness (Tate et al., 2005). More recent studies, especially in the Chinese context, Rowe et al. (2014) proposed the measurement of urban intensity from variables of compactness, density, diversity, and connectivity, aiming at revealing the resource distribution, transportation efficiency, and social integration in both cities and optimize the urban performance (Rowe et al., 2014). Later, Guan and Rowe (2016) evaluated the spatial structure of small towns in Zhejiang Province using similar urban intensity characteristics, such as density, compactness, diversity, and accessibility.
Research framework
The research aims to apply sentiment analysis to quantify qualitative public feelings from text-based information as a representation of individual real-time happiness, and develop a multivariate model to explore the relationship between the individual feelings from social media and the built environment in China. Based on the model, this research compare existing condition and the planned condition of Jiaxing city in China, and test different planning scenarios to explore the possible changes of individual feeling when using social media platform.
Methodology
Site
The study area may be Jiaxing city in China’s Zhejiang province. Jiaxing is a significant city that is part of the Yangtze River Delta city cluster and the Shanghai metropolitan area. It is located in close proximity to the two major cities of Shanghai and Hangzhou. It is a small tourist city with two counties, 44 towns, and 809 administrative villages that has been designated as a national key program.
Data collection and preprocess
For this research, social media data (Weibo posts) was collected from Sina Weibo, which is one of the most used online social platform in China. The collected Weibo posts are restricted with the tag “Jiaxing” in 2018, which limited the posts related to the case city. The untreated data included a large amount of advertisements, which have been removed by identifying certain keywords such as “advertisement”, “cost performance”, etc. The urban amenity data is collected from Gaode Map as spatial POI (points of interest). The regulatory detailed planning (RDP) documents from 2017-2020 issued by the local government in Jiaxing are collected from the local government office.
Proximity to urban amenities
In this research, proximity is defined by the accessibility of urban activities and amenities by walking and by characteristics of pedestrian networks. The amenities dataset is collected as points of interest (POIs) from Gaodu Map in China, which is one of the most widely used digital navigation systems in China. The urban networks are extracted from the OpenStreet Map (OSM). Accessibility will be calculated by the R5 package embedded in the R studio.